[Numpy-discussion] numpy.array does not take generators
barrywark at gmail.com
Fri Aug 17 19:00:58 EDT 2007
Is there a reason not to add an argument to fromiter that specifies
the final size of the n-d array? Reading this discussion, I realized
that there are several places in my code where I create 2-D arrays
arr = N.array([d.data() for d in list_of_data_containers]),
where d.data() returns a buffer object.
I would guess that this paradigm causes lots of memory copying. The
more efficient solution, I think, would be to preallocate the array
and then assign each row in a loop. It's so much clearer this way,
however, that I've kept it as is in the code.
So, what if I could do something like
arr = N.fromiter(d.data() for d in list_of_data_containers, shape=(x,y)),
with the contract that fromiter will throw an exception if any of the
d.data() are not of size y or if there are more than x elements in
Just a thought for discussion.
On 8/16/07, Robert Kern <robert.kern at gmail.com> wrote:
> Geoffrey Zhu wrote:
> > Hi All,
> > I want to construct a numpy array based on Python objects. In the
> > below code, opts is a list of tuples.
> > For example,
> > opts=[ ('C', 100, 3, 'A'), ('K', 200, 5.4, 'B')]
> > If I use a generator like the following:
> > K=numpy.array(o/1000.0 for o in opts)
> > It does not work.
> > I have to use:
> > numpy.array([o/1000.0 for o in opts])
> > Is this behavior intended?
> Yes. With arbitrary generators, there is no good way to do the kind of
> mind-reading that numpy.array() usually does with sequences. It would have to
> unroll the whole generator anyways. fromiter() works for this, but you are
> restricted to 1-D arrays which is a lot easier to implement the mind-reading for.
> Robert Kern
> "I have come to believe that the whole world is an enigma, a harmless enigma
> that is made terrible by our own mad attempt to interpret it as though it had
> an underlying truth."
> -- Umberto Eco
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